Abstract

High density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the number of points that need to be processed in subsequent steps, i.e., the extraction of individual pylons and wires. However, the existing solutions mostly overlook this advantage by processing all of the available data at one time, which hinders their application to large datasets. Moreover, the presence of high vegetation and hilly terrain may challenge many of the existing methods, since vertically overlapping objects (e.g., trees and wires) may not be effectively segmented using a single height threshold. For extraction of pylons and wires, this paper proposes a novel approach which involves converting the input points at different height levels into binary masks. Long straight lines are extracted from these masks and convex hulls around the lines at individual height levels are used to form series of hulls across the height levels. The series of hulls are then projected onto a horizontal plane to form individual corridors. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. The seed regions thereafter are grown to extract the complete pylons. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. When tested on two large Australian datasets, the proposed approach exhibited high object-based performance (correctness for pylons and wires of 100% and 99.6%, respectively) and high point-based performance (completeness for pylons and wires of 98.1% and 95%, respectively). Moreover, the planimetric accuracy for the detected pylons was 0.10 m. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires.

Highlights

  • The reconstruction of an electrical power line corridor (PLC) is important in many applications including detection of potential hazards, such as vegetation encroachment [1] and analysis of power line (PL, wire) structural stability [2]

  • This paper presents a novel approach where PLCs, pylons and wires are extracted in order

  • This paper presents a new approach for extraction of corridors, pylons and wires

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Summary

Introduction

The reconstruction of an electrical power line corridor (PLC) is important in many applications including detection of potential hazards, such as vegetation encroachment [1] and analysis of power line (PL, wire) structural stability [2]. PLCs are surveyed in person or by manually inspecting aerial photos and videos. Periodic inspection of thousands of kilometres of PLCs is time consuming and labour intensive, and prone to error due to the involvement of human judgement. The advent of airborne laser scanning technology, known as LIght Detection And Ranging (LIDAR), allows dense point cloud data to be collected which has made the survey more efficient and more economic. Millions of 3D points on the Earth’s surface, both natural and structural, are collected and automatically processed off-line on powerful computers. A recent comprehensive review of various types of PLC surveying methods can be found in the work of Matikainen et al [3]

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